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Found 6 papers, 1 papers with code
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?
Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods.
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
Recent work has argued that classification losses utilizing softmax cross-entropy are superior not only for fixed-set classification tasks, but also by outperforming losses developed specifically for open-set tasks including few-shot learning and retrieval.
Modeling Uncertainty with Hedged Instance Embeddings
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image.
VIP: Finding Important People in Images
We address two specific questions -- Given an image, who are the most important individuals in it?
What's in a Name? First Names as Facial Attributes
We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers.
